{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ONNX ResNet Model\n",
    "\n",
    "This example will show inference over an exported [ONNX](https://github.com/onnx/onnx) ResNet model using Seldon Core. We will use the Seldon S2I wrapper for Intel's NGraph. The example follows this [NGraph tutorial](https://ai.intel.com/adaptable-deep-learning-solutions-with-ngraph-compiler-and-onnx/).\n",
    "\n",
    " Prerequisites:\n",
    "   * ```pip install seldon-core```\n",
    "   * To test locally [ngraph installed](https://github.com/NervanaSystems/ngraph-onnx)\n",
    "   * protoc > 3.4.0\n",
    "   \n",
    "To run all of the notebook successfully you will need to start it with\n",
    "```\n",
    "jupyter notebook --NotebookApp.iopub_data_rate_limit=100000000\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download ResNet model fron ONNX Zoo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2019-04-24 15:34:51--  https://s3.amazonaws.com/download.onnx/models/opset_8/resnet50.tar.gz\n",
      "Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.216.160.221\n",
      "Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.216.160.221|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 101706397 (97M) [binary/octet-stream]\n",
      "Saving to: ‘resnet50.tar.gz’\n",
      "\n",
      "resnet50.tar.gz     100%[===================>]  96.99M  8.23MB/s    in 25s     \n",
      "\n",
      "2019-04-24 15:35:16 (3.90 MB/s) - ‘resnet50.tar.gz’ saved [101706397/101706397]\n",
      "\n",
      "resnet50/\n",
      "resnet50/test_data_0.npz\n",
      "resnet50/test_data_2.npz\n",
      "resnet50/test_data_1.npz\n",
      "resnet50/test_data_set_0/\n",
      "resnet50/test_data_set_0/input_0.pb\n",
      "resnet50/test_data_set_0/output_0.pb\n",
      "resnet50/test_data_set_1/\n",
      "resnet50/test_data_set_1/input_0.pb\n",
      "resnet50/test_data_set_1/output_0.pb\n",
      "resnet50/test_data_set_2/\n",
      "resnet50/test_data_set_2/input_0.pb\n",
      "resnet50/test_data_set_2/output_0.pb\n",
      "resnet50/test_data_set_3/\n",
      "resnet50/test_data_set_3/input_0.pb\n",
      "resnet50/test_data_set_3/output_0.pb\n",
      "resnet50/test_data_set_4/\n",
      "resnet50/test_data_set_4/input_0.pb\n",
      "resnet50/test_data_set_4/output_0.pb\n",
      "resnet50/test_data_set_5/\n",
      "resnet50/test_data_set_5/input_0.pb\n",
      "resnet50/test_data_set_5/output_0.pb\n",
      "resnet50/test_data_set_6/\n",
      "resnet50/test_data_set_6/input_0.pb\n",
      "resnet50/test_data_set_6/output_0.pb\n",
      "resnet50/test_data_set_7/\n",
      "resnet50/test_data_set_7/input_0.pb\n",
      "resnet50/test_data_set_7/output_0.pb\n",
      "resnet50/test_data_set_8/\n",
      "resnet50/test_data_set_8/input_0.pb\n",
      "resnet50/test_data_set_8/output_0.pb\n",
      "resnet50/model.onnx\n"
     ]
    }
   ],
   "source": [
    "!wget https://s3.amazonaws.com/download.onnx/models/opset_8/resnet50.tar.gz\n",
    "!tar -xzvf resnet50.tar.gz\n",
    "!rm resnet50.tar.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from keras.applications.imagenet_utils import decode_predictions\n",
    "from keras.applications.resnet50 import preprocess_input\n",
    "from keras.preprocessing import image\n",
    "import ngraph as ng\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Model\n",
    "Load ONNX model into ngraph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ONNX `ai.onnx` opset version 8 is not supported. Falling back to latest supported version: 7\n",
      "More than one different shape in input nodes [<Constant: 'Constant_289' ([])>, <BatchNormInference: 'gpu_0/res2_0_branch2c_bn_1' ([1, 256, 56, 56])>].\n",
      "More than one different shape in input nodes [<Constant: 'Constant_308' ([])>, <BatchNormInference: 'gpu_0/res2_1_branch2c_bn_1' ([1, 256, 56, 56])>].\n",
      "More than one different shape in input nodes [<Constant: 'Constant_327' ([])>, <BatchNormInference: 'gpu_0/res2_2_branch2c_bn_1' ([1, 256, 56, 56])>].\n",
      "More than one different shape in input nodes [<Constant: 'Constant_349' ([])>, <BatchNormInference: 'gpu_0/res3_0_branch2c_bn_1' ([1, 512, 28, 28])>].\n",
      "More than one different shape in input nodes [<Constant: 'Constant_368' ([])>, <BatchNormInference: 'gpu_0/res3_1_branch2c_bn_1' ([1, 512, 28, 28])>].\n",
      "More than one different shape in input nodes [<Constant: 'Constant_387' ([])>, <BatchNormInference: 'gpu_0/res3_2_branch2c_bn_1' ([1, 512, 28, 28])>].\n",
      "More than one different shape in input nodes [<Constant: 'Constant_406' ([])>, <BatchNormInference: 'gpu_0/res3_3_branch2c_bn_1' ([1, 512, 28, 28])>].\n",
      "More than one different shape in input nodes [<Constant: 'Constant_428' ([])>, <BatchNormInference: 'gpu_0/res4_0_branch2c_bn_1' ([1, 1024, 14, 14])>].\n",
      "More than one different shape in input nodes [<Constant: 'Constant_447' ([])>, <BatchNormInference: 'gpu_0/res4_1_branch2c_bn_1' ([1, 1024, 14, 14])>].\n",
      "More than one different shape in input nodes [<Constant: 'Constant_466' ([])>, <BatchNormInference: 'gpu_0/res4_2_branch2c_bn_1' ([1, 1024, 14, 14])>].\n",
      "More than one different shape in input nodes [<Constant: 'Constant_485' ([])>, <BatchNormInference: 'gpu_0/res4_3_branch2c_bn_1' ([1, 1024, 14, 14])>].\n",
      "More than one different shape in input nodes [<Constant: 'Constant_504' ([])>, <BatchNormInference: 'gpu_0/res4_4_branch2c_bn_1' ([1, 1024, 14, 14])>].\n",
      "More than one different shape in input nodes [<Constant: 'Constant_523' ([])>, <BatchNormInference: 'gpu_0/res4_5_branch2c_bn_1' ([1, 1024, 14, 14])>].\n",
      "More than one different shape in input nodes [<Constant: 'Constant_545' ([])>, <BatchNormInference: 'gpu_0/res5_0_branch2c_bn_1' ([1, 2048, 7, 7])>].\n",
      "More than one different shape in input nodes [<Constant: 'Constant_564' ([])>, <BatchNormInference: 'gpu_0/res5_1_branch2c_bn_1' ([1, 2048, 7, 7])>].\n",
      "More than one different shape in input nodes [<Constant: 'Constant_583' ([])>, <BatchNormInference: 'gpu_0/res5_2_branch2c_bn_1' ([1, 2048, 7, 7])>].\n"
     ]
    }
   ],
   "source": [
    "from ngraph_onnx.onnx_importer.importer import import_onnx_file\n",
    "\n",
    "# Import the ONNX file\n",
    "models = import_onnx_file('resnet50/model.onnx')\n",
    "\n",
    "# Create an nGraph runtime environment\n",
    "runtime = ng.runtime(backend_name='CPU')\n",
    "\n",
    "# Select the first model and compile it to a callable function\n",
    "model = models[0]\n",
    "resnet = runtime.computation(model['output'], *model['inputs'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test on an image of a Zebra."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[('n02391049', 'zebra', 0.99543387),\n",
       "  ('n02423022', 'gazelle', 0.0006074826),\n",
       "  ('n01518878', 'ostrich', 0.0005718305),\n",
       "  ('n02422106', 'hartebeest', 0.00048323255),\n",
       "  ('n02422699', 'impala', 0.0003381068)]]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa61c686208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = image.load_img('zebra.jpg', target_size=(224, 224))\n",
    "img = image.img_to_array(img)\n",
    "plt.imshow(img / 255.)\n",
    "x = np.expand_dims(img.copy(), axis=0)\n",
    "x = preprocess_input(x,mode='torch')\n",
    "x = x.transpose(0,3,1,2)\n",
    "preds = resnet(x)\n",
    "decode_predictions(preds[0], top=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Package Model Using S2I"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---> Installing application source...\n",
      "---> Installing dependencies ...\n",
      "Collecting keras (from -r requirements.txt (line 1))\n",
      "Downloading https://files.pythonhosted.org/packages/5e/10/aa32dad071ce52b5502266b5c659451cfd6ffcbf14e6c8c4f16c0ff5aaab/Keras-2.2.4-py2.py3-none-any.whl (312kB)\n",
      "Requirement already satisfied: Keras-Preprocessing in /usr/local/lib/python3.6/site-packages (from -r requirements.txt (line 2)) (1.0.5)\n",
      "Collecting pillow (from -r requirements.txt (line 3))\n",
      "Downloading https://files.pythonhosted.org/packages/d2/c2/f84b1e57416755e967236468dcfb0fad7fd911f707185efc4ba8834a1a94/Pillow-6.0.0-cp36-cp36m-manylinux1_x86_64.whl (2.0MB)\n",
      "Collecting pyyaml (from keras->-r requirements.txt (line 1))\n",
      "Downloading https://files.pythonhosted.org/packages/9f/2c/9417b5c774792634834e730932745bc09a7d36754ca00acf1ccd1ac2594d/PyYAML-5.1.tar.gz (274kB)\n",
      "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/site-packages (from keras->-r requirements.txt (line 1)) (1.0.6)\n",
      "Requirement already satisfied: h5py in /usr/local/lib/python3.6/site-packages (from keras->-r requirements.txt (line 1)) (2.8.0)\n",
      "Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.6/site-packages (from keras->-r requirements.txt (line 1)) (1.15.4)\n",
      "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.6/site-packages (from keras->-r requirements.txt (line 1)) (1.12.0)\n",
      "Collecting scipy>=0.14 (from keras->-r requirements.txt (line 1))\n",
      "Downloading https://files.pythonhosted.org/packages/7f/5f/c48860704092933bf1c4c1574a8de1ffd16bf4fde8bab190d747598844b2/scipy-1.2.1-cp36-cp36m-manylinux1_x86_64.whl (24.8MB)\n",
      "Building wheels for collected packages: pyyaml\n",
      "Running setup.py bdist_wheel for pyyaml: started\n",
      "Running setup.py bdist_wheel for pyyaml: finished with status 'done'\n",
      "Stored in directory: /root/.cache/pip/wheels/ad/56/bc/1522f864feb2a358ea6f1a92b4798d69ac783a28e80567a18b\n",
      "Successfully built pyyaml\n",
      "Installing collected packages: pyyaml, scipy, keras, pillow\n",
      "Successfully installed keras-2.2.4 pillow-6.0.0 pyyaml-5.1 scipy-1.2.1\n",
      "You are using pip version 18.1, however version 19.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\n",
      "Build completed successfully\n"
     ]
    }
   ],
   "source": [
    "!s2i build . seldonio/seldon-core-s2i-python3-ngraph-onnx:0.3 onnx-resnet:0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2cb7af654473d83aadacbcf4167271161a81cbba6d7919d8f42f3b724974312a\r\n"
     ]
    }
   ],
   "source": [
    "!docker run --name \"onnx_resnet_predictor\" -d --rm -p 5000:5000 onnx-resnet:0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------\n",
      "SENDING NEW REQUEST:\n",
      "\n",
      "[[[[0.888 0.714 0.452 ... 0.64  0.367 0.288]\n",
      "   [0.981 0.44  0.077 ... 0.636 0.331 0.513]\n",
      "   [0.613 0.36  0.413 ... 0.418 0.669 0.955]\n",
      "   ...\n",
      "   [0.604 0.321 0.569 ... 0.789 0.239 0.127]\n",
      "   [0.081 0.786 0.974 ... 0.364 0.595 0.149]\n",
      "   [0.037 0.486 0.697 ... 0.353 0.884 0.221]]\n",
      "\n",
      "  [[0.237 0.926 0.605 ... 0.461 0.767 0.483]\n",
      "   [0.527 0.894 0.212 ... 0.443 0.285 0.409]\n",
      "   [0.983 0.521 0.208 ... 0.693 0.671 0.683]\n",
      "   ...\n",
      "   [0.333 0.45  0.674 ... 0.361 0.506 0.067]\n",
      "   [0.385 0.096 0.811 ... 0.464 0.023 0.869]\n",
      "   [0.318 0.46  0.452 ... 0.806 0.769 0.316]]\n",
      "\n",
      "  [[0.633 0.982 0.495 ... 0.928 0.775 0.81 ]\n",
      "   [0.54  0.098 0.56  ... 0.409 0.621 0.758]\n",
      "   [0.959 0.571 0.152 ... 0.432 0.323 0.596]\n",
      "   ...\n",
      "   [0.159 0.469 0.975 ... 0.165 0.135 0.312]\n",
      "   [0.685 0.859 0.111 ... 0.382 0.943 0.715]\n",
      "   [0.165 0.453 0.042 ... 0.032 0.666 0.48 ]]]]\n",
      "RECEIVED RESPONSE:\n",
      "meta {\n",
      "}\n",
      "data {\n",
      "  names: \"t:0\"\n",
      "  ndarray {\n",
      "    values {\n",
      "      list_value {\n",
      "        values {\n",
      "          list_value {\n",
      "            values {\n",
      "              number_value: 0.00019308735500089824\n",
      "            }\n",
      "            values {\n",
      "              number_value: 0.0006821825518272817\n",
      "            }\n",
      "            values {\n",
      "              number_value: 7.613330672029406e-05\n",
      "            }\n",
      "            values {\n",
      "              number_value: 4.23646233684849e-05\n",
      "            }\n",
      "            values {\n",
      "              number_value: 0.00014747105888091028\n",
      "            }\n",
      "            values {\n",
      "              number_value: 0.00018536749121267349\n",
      "            }\n",
      "            values {\n",
      "              number_value: 2.3800781491445377e-05\n",
      "            }\n",
      "            values {\n",
      "              number_value: 0.00022915711451787502\n",
      "            }\n",
      "            values {\n",
      "              number_value: 5.7388722780160606e-05\n",
      "            }\n",
      "            values {\n",
      "              number_value: 0.0006487827049568295\n",
      "            }\n",
      "            values {\n",
      "              number_value: 0.0006237452616915107\n",
      "            }\n",
      "            values {\n",
      "              number_value: 0.00013771375233773142\n",
      "            }\n",
      "            values {\n",
      "              number_value: 0.00013485578529071063\n",
      "            }\n",
      "            values {\n",
      "              number_value: 0.00021599233150482178\n",
      "            }\n",
      "            values {\n",
      "              number_value: 3.910527811967768e-05\n",
      "            }\n",
      "            values {\n",
      "              number_value: 0.00019672788039315492\n",
      "            }\n",
      "            values {\n",
      "              number_value: 0.0005595136899501085\n",
      "            }\n",
      "            values {\n",
      "              number_value: 0.00026847218396142125\n",
      "            }\n",
      "            values {\n",
      "              number_value: 0.00022938931942917407\n",
      "            }\n",
      "            values {\n",
      "              number_value: 9.010926441987976e-05\n",
      "            }\n",
      "            values {\n",
      "              number_value: 0.0002063583815470338\n",
      "            }\n",
      "            values {\n",
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      "            values {\n",
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      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!seldon-core-tester contract.json 0.0.0.0 5000 -p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "onnx_resnet_predictor\r\n"
     ]
    }
   ],
   "source": [
    "!docker rm onnx_resnet_predictor --force"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test using Minikube\n",
    "\n",
    "**Due to a [minikube/s2i issue](https://github.com/SeldonIO/seldon-core/issues/253) you will need [s2i >= 1.1.13](https://github.com/openshift/source-to-image/releases/tag/v1.1.13)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "😄  minikube v0.34.1 on linux (amd64)\n",
      "🔥  Creating virtualbox VM (CPUs=2, Memory=4096MB, Disk=20000MB) ...\n",
      "📶  \"minikube\" IP address is 192.168.99.100\n",
      "🐳  Configuring Docker as the container runtime ...\n",
      "✨  Preparing Kubernetes environment ...\n",
      "🚜  Pulling images required by Kubernetes v1.13.3 ...\n",
      "🚀  Launching Kubernetes v1.13.3 using kubeadm ... \n",
      "🔑  Configuring cluster permissions ...\n",
      "🤔  Verifying component health .....\n",
      "💗  kubectl is now configured to use \"minikube\"\n",
      "🏄  Done! Thank you for using minikube!\n"
     ]
    }
   ],
   "source": [
    "!minikube start --memory 4096"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clusterrolebinding.rbac.authorization.k8s.io/kube-system-cluster-admin created\r\n"
     ]
    }
   ],
   "source": [
    "!kubectl create clusterrolebinding kube-system-cluster-admin --clusterrole=cluster-admin --serviceaccount=kube-system:default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$HELM_HOME has been configured at /home/clive/.helm.\n",
      "\n",
      "Tiller (the Helm server-side component) has been installed into your Kubernetes Cluster.\n",
      "\n",
      "Please note: by default, Tiller is deployed with an insecure 'allow unauthenticated users' policy.\n",
      "To prevent this, run `helm init` with the --tiller-tls-verify flag.\n",
      "For more information on securing your installation see: https://docs.helm.sh/using_helm/#securing-your-helm-installation\n",
      "Happy Helming!\n"
     ]
    }
   ],
   "source": [
    "!helm init"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Waiting for deployment \"tiller-deploy\" rollout to finish: 0 of 1 updated replicas are available...\n",
      "deployment \"tiller-deploy\" successfully rolled out\n"
     ]
    }
   ],
   "source": [
    "!kubectl rollout status deploy/tiller-deploy -n kube-system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NAME:   seldon-core\n",
      "LAST DEPLOYED: Wed Apr 24 16:04:55 2019\n",
      "NAMESPACE: seldon-system\n",
      "STATUS: DEPLOYED\n",
      "\n",
      "RESOURCES:\n",
      "==> v1/ClusterRole\n",
      "NAME                          AGE\n",
      "seldon-operator-manager-role  0s\n",
      "\n",
      "==> v1/ClusterRoleBinding\n",
      "NAME                                 AGE\n",
      "seldon-operator-manager-rolebinding  0s\n",
      "\n",
      "==> v1/Service\n",
      "NAME                                        TYPE       CLUSTER-IP   EXTERNAL-IP  PORT(S)  AGE\n",
      "seldon-operator-controller-manager-service  ClusterIP  10.109.6.84  <none>       443/TCP  0s\n",
      "\n",
      "==> v1/StatefulSet\n",
      "NAME                                DESIRED  CURRENT  AGE\n",
      "seldon-operator-controller-manager  1        1        0s\n",
      "\n",
      "==> v1/Pod(related)\n",
      "NAME                                  READY  STATUS             RESTARTS  AGE\n",
      "seldon-operator-controller-manager-0  0/1    ContainerCreating  0         0s\n",
      "\n",
      "==> v1/Secret\n",
      "NAME                                   TYPE    DATA  AGE\n",
      "seldon-operator-webhook-server-secret  Opaque  0     0s\n",
      "\n",
      "==> v1beta1/CustomResourceDefinition\n",
      "NAME                                         AGE\n",
      "seldondeployments.machinelearning.seldon.io  0s\n",
      "\n",
      "\n",
      "NOTES:\n",
      "NOTES: TODO\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!helm install ../../../helm-charts/seldon-core-operator --name seldon-core --set usageMetrics.enabled=true   --namespace seldon-system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "partitioned roll out complete: 1 new pods have been updated...\r\n"
     ]
    }
   ],
   "source": [
    "!kubectl rollout status deploy/seldon-controller-manager -n seldon-system"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup Ingress\n",
    "Please note: There are reported gRPC issues with ambassador (see https://github.com/SeldonIO/seldon-core/issues/473)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NAME:   ambassador\n",
      "LAST DEPLOYED: Wed Apr 24 16:05:49 2019\n",
      "NAMESPACE: seldon\n",
      "STATUS: DEPLOYED\n",
      "\n",
      "RESOURCES:\n",
      "==> v1/Service\n",
      "NAME               TYPE          CLUSTER-IP     EXTERNAL-IP  PORT(S)                     AGE\n",
      "ambassador-admins  ClusterIP     10.100.104.84  <none>       8877/TCP                    0s\n",
      "ambassador         LoadBalancer  10.100.39.238  <pending>    80:30316/TCP,443:30972/TCP  0s\n",
      "\n",
      "==> v1/Deployment\n",
      "NAME        DESIRED  CURRENT  UP-TO-DATE  AVAILABLE  AGE\n",
      "ambassador  3        3        3           0          0s\n",
      "\n",
      "==> v1/Pod(related)\n",
      "NAME                         READY  STATUS             RESTARTS  AGE\n",
      "ambassador-5b89d44544-6nr2m  0/1    ContainerCreating  0         0s\n",
      "ambassador-5b89d44544-9xp9c  0/1    ContainerCreating  0         0s\n",
      "ambassador-5b89d44544-hhd49  0/1    ContainerCreating  0         0s\n",
      "\n",
      "==> v1/ServiceAccount\n",
      "NAME        SECRETS  AGE\n",
      "ambassador  1        0s\n",
      "\n",
      "==> v1beta1/ClusterRole\n",
      "NAME        AGE\n",
      "ambassador  0s\n",
      "\n",
      "==> v1beta1/ClusterRoleBinding\n",
      "NAME        AGE\n",
      "ambassador  0s\n",
      "\n",
      "\n",
      "NOTES:\n",
      "Congratuations! You've successfully installed Ambassador.\n",
      "\n",
      "For help, visit our Slack at https://d6e.co/slack or view the documentation online at https://www.getambassador.io.\n",
      "\n",
      "To get the IP address of Ambassador, run the following commands:\n",
      "NOTE: It may take a few minutes for the LoadBalancer IP to be available.\n",
      "     You can watch the status of by running 'kubectl get svc -w  --namespace seldon ambassador'\n",
      "\n",
      "  On GKE/Azure:\n",
      "  export SERVICE_IP=$(kubectl get svc --namespace seldon ambassador -o jsonpath='{.status.loadBalancer.ingress[0].ip}')\n",
      "\n",
      "  On AWS:\n",
      "  export SERVICE_IP=$(kubectl get svc --namespace seldon ambassador -o jsonpath='{.status.loadBalancer.ingress[0].hostname}')\n",
      "\n",
      "  echo http://$SERVICE_IP:\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!helm install stable/ambassador --name ambassador --set crds.keep=false"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Waiting for deployment \"ambassador\" rollout to finish: 0 of 3 updated replicas are available...\n",
      "Waiting for deployment \"ambassador\" rollout to finish: 1 of 3 updated replicas are available...\n",
      "Waiting for deployment \"ambassador\" rollout to finish: 2 of 3 updated replicas are available...\n",
      "deployment \"ambassador\" successfully rolled out\n"
     ]
    }
   ],
   "source": [
    "!kubectl rollout status deployment.apps/ambassador"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---> Installing application source...\n",
      "---> Installing dependencies ...\n",
      "Collecting keras (from -r requirements.txt (line 1))\n",
      "Downloading https://files.pythonhosted.org/packages/5e/10/aa32dad071ce52b5502266b5c659451cfd6ffcbf14e6c8c4f16c0ff5aaab/Keras-2.2.4-py2.py3-none-any.whl (312kB)\n",
      "Requirement already satisfied: Keras-Preprocessing in /usr/local/lib/python3.6/site-packages (from -r requirements.txt (line 2)) (1.0.5)\n",
      "Collecting pillow (from -r requirements.txt (line 3))\n",
      "Downloading https://files.pythonhosted.org/packages/d2/c2/f84b1e57416755e967236468dcfb0fad7fd911f707185efc4ba8834a1a94/Pillow-6.0.0-cp36-cp36m-manylinux1_x86_64.whl (2.0MB)\n",
      "Collecting scipy>=0.14 (from keras->-r requirements.txt (line 1))\n",
      "Downloading https://files.pythonhosted.org/packages/7f/5f/c48860704092933bf1c4c1574a8de1ffd16bf4fde8bab190d747598844b2/scipy-1.2.1-cp36-cp36m-manylinux1_x86_64.whl (24.8MB)\n",
      "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/site-packages (from keras->-r requirements.txt (line 1)) (1.0.6)\n",
      "Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.6/site-packages (from keras->-r requirements.txt (line 1)) (1.15.4)\n",
      "Requirement already satisfied: h5py in /usr/local/lib/python3.6/site-packages (from keras->-r requirements.txt (line 1)) (2.8.0)\n",
      "Collecting pyyaml (from keras->-r requirements.txt (line 1))\n",
      "Downloading https://files.pythonhosted.org/packages/9f/2c/9417b5c774792634834e730932745bc09a7d36754ca00acf1ccd1ac2594d/PyYAML-5.1.tar.gz (274kB)\n",
      "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.6/site-packages (from keras->-r requirements.txt (line 1)) (1.12.0)\n",
      "Building wheels for collected packages: pyyaml\n",
      "Running setup.py bdist_wheel for pyyaml: started\n",
      "Running setup.py bdist_wheel for pyyaml: finished with status 'done'\n",
      "Stored in directory: /root/.cache/pip/wheels/ad/56/bc/1522f864feb2a358ea6f1a92b4798d69ac783a28e80567a18b\n",
      "Successfully built pyyaml\n",
      "Installing collected packages: scipy, pyyaml, keras, pillow\n",
      "Successfully installed keras-2.2.4 pillow-6.0.0 pyyaml-5.1 scipy-1.2.1\n",
      "You are using pip version 18.1, however version 19.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\n",
      "Build completed successfully\n"
     ]
    }
   ],
   "source": [
    "!eval $(minikube docker-env) && s2i build . seldonio/seldon-core-s2i-python3-ngraph-onnx:0.3 onnx-resnet:0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seldondeployment.machinelearning.seldon.io/seldon-deployment-example created\r\n"
     ]
    }
   ],
   "source": [
    "!kubectl create -f onnx_resnet_deployment.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Waiting for deployment \"onnx-resnet-deployment-onnx-resnet-predictor-21cdd95\" rollout to finish: 0 of 1 updated replicas are available...\n",
      "deployment \"onnx-resnet-deployment-onnx-resnet-predictor-21cdd95\" successfully rolled out\n"
     ]
    }
   ],
   "source": [
    "!kubectl rollout status deploy/onnx-resnet-deployment-onnx-resnet-predictor-21cdd95"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------\n",
      "SENDING NEW REQUEST:\n",
      "\n",
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      "   ...\n",
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      "\n",
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      "   [0.421 0.057 0.844 ... 0.707 0.808 0.681]\n",
      "   ...\n",
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      "\n",
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      "   ...\n",
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      "   [0.444 0.08  0.437 ... 0.978 0.376 0.299]]]]\n",
      "RECEIVED RESPONSE:\n",
      "meta {\n",
      "  puid: \"5ohh1g7k6rnhbmu8ds8bs1nj7c\"\n",
      "  requestPath {\n",
      "    key: \"onnx-resnet-classifier\"\n",
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      "              number_value: 0.00013198975648265332\n",
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      "            values {\n",
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      "            values {\n",
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      "            values {\n",
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      "            values {\n",
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      "            values {\n",
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      "            values {\n",
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      "            values {\n",
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      "            values {\n",
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      "            values {\n",
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      "            values {\n",
      "              number_value: 0.00039621745236217976\n",
      "            }\n",
      "            values {\n",
      "              number_value: 0.0002872168261092156\n",
      "            }\n",
      "            values {\n",
      "              number_value: 0.00029204797465354204\n",
      "            }\n",
      "            values {\n",
      "              number_value: 0.00020918257359880954\n",
      "            }\n",
      "            values {\n",
      "              number_value: 0.0002427437575533986\n",
      "            }\n",
      "            values {\n",
      "              number_value: 0.0002806625852826983\n",
      "            }\n",
      "            values {\n",
      "              number_value: 6.33000599918887e-05\n",
      "            }\n",
      "            values {\n",
      "              number_value: 0.00019161614181939512\n",
      "            }\n",
      "            values {\n",
      "              number_value: 0.0005559056880883873\n",
      "            }\n",
      "            values {\n",
      "              number_value: 0.00042272970313206315\n",
      "            }\n",
      "            values {\n",
      "              number_value: 0.0013687640894204378\n",
      "            }\n",
      "            values {\n",
      "              number_value: 9.02428146218881e-05\n",
      "            }\n",
      "            values {\n",
      "              number_value: 5.963832518318668e-05\n",
      "            }\n",
      "            values {\n",
      "              number_value: 9.348502499051392e-05\n",
      "            }\n",
      "            values {\n",
      "              number_value: 5.510474875336513e-05\n",
      "            }\n",
      "            values {\n",
      "              number_value: 4.4944488763576373e-05\n",
      "            }\n",
      "            values {\n",
      "              number_value: 4.4385909859556705e-05\n",
      "            }\n",
      "            values {\n",
      "              number_value: 4.139883822062984e-05\n",
      "            }\n",
      "            values {\n",
      "              number_value: 0.0003389321791473776\n",
      "            }\n",
      "            values {\n",
      "              number_value: 0.0005634939298033714\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!seldon-core-api-tester contract.json `minikube ip` `kubectl get svc ambassador -o jsonpath='{.spec.ports[0].nodePort}'` \\\n",
    "    seldon-deployment-example --namespace seldon -p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!minikube delete\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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